|
|
--- |
|
|
library_name: peft |
|
|
license: mit |
|
|
base_model: microsoft/Phi-3.5-mini-instruct |
|
|
tags: |
|
|
- axolotl |
|
|
- generated_from_trainer |
|
|
model-index: |
|
|
- name: curator_math_phase1_sn_ensemble7_90325 |
|
|
results: [] |
|
|
--- |
|
|
|
|
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
|
|
should probably proofread and complete it, then remove this comment. --> |
|
|
|
|
|
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) |
|
|
<details><summary>See axolotl config</summary> |
|
|
|
|
|
axolotl version: `0.5.0` |
|
|
|
|
|
|
|
|
</details><br> |
|
|
|
|
|
# Collinear Curator 1: |
|
|
|
|
|
This is an open-source fine-tuned reasoning adapter of [microsoft/Phi-3.5-mini-instruct](https://huggingface.co/microsoft/Phi-3.5-mini-instruct), transformed into a math reasoning model using data curated from [collinear-ai/R1-Distill-SFT-Curated](https://huggingface.co/datasets/collinear-ai/R1-Distill-SFT-Curated). |
|
|
It achieves the following results on the evaluation set: |
|
|
- Loss: 0.3203 |
|
|
|
|
|
## Model description |
|
|
|
|
|
This model is a LoRA adaptor and for best results merge it with base model [microsoft/Phi-3.5-mini-instruct](https://huggingface.co/microsoft/Phi-3.5-mini-instruct) before use. |
|
|
|
|
|
|
|
|
## Training and evaluation data |
|
|
|
|
|
- Training data: [collinear-ai/R1-Distill-SFT-Curated](https://huggingface.co/datasets/collinear-ai/R1-Distill-SFT-Curated) |
|
|
- Evaluation data: [HuggingFaceH4/MATH-500](https://huggingface.co/datasets/HuggingFaceH4/MATH-500) |
|
|
|
|
|
|
|
|
## Training procedure |
|
|
|
|
|
### Training hyperparameters |
|
|
|
|
|
The following hyperparameters were used during training: |
|
|
- learning_rate: 5e-06 |
|
|
- train_batch_size: 8 |
|
|
- eval_batch_size: 8 |
|
|
- seed: 42 |
|
|
- distributed_type: multi-GPU |
|
|
- num_devices: 8 |
|
|
- gradient_accumulation_steps: 2 |
|
|
- total_train_batch_size: 128 |
|
|
- total_eval_batch_size: 64 |
|
|
- optimizer: Use OptimizerNames.PAGED_ADAMW_8BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
|
|
- lr_scheduler_type: cosine |
|
|
- lr_scheduler_warmup_steps: 50 |
|
|
- num_epochs: 1 |
|
|
|
|
|
### Training results |
|
|
|
|
|
| Training Loss | Epoch | Step | Validation Loss | |
|
|
|:-------------:|:------:|:----:|:---------------:| |
|
|
| No log | 0.0003 | 1 | 0.6714 | |
|
|
| 0.337 | 0.3335 | 1243 | 0.3361 | |
|
|
| 0.3248 | 0.6669 | 2486 | 0.3203 | |
|
|
|
|
|
|
|
|
### Evaluation Results on Math500 |
|
|
The following figure shows the accuracy and the speedup of Collinear Curators C1 and C2 when compared to training on unfiltered dataset. |
|
|
 |
|
|
|
|
|
|
|
|
### Framework versions |
|
|
|
|
|
- PEFT 0.13.2 |
|
|
- Transformers 4.46.1 |
|
|
- Pytorch 2.3.1+cu121 |
|
|
- Datasets 3.0.1 |
|
|
- Tokenizers 0.20.3 |
